Overview

Dataset statistics

Number of variables16
Number of observations157
Missing cells51
Missing cells (%)2.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.8 KiB
Average record size in memory128.8 B

Variable types

Categorical4
Numeric12

Alerts

Model has a high cardinality: 156 distinct values High cardinality
Latest_Launch has a high cardinality: 130 distinct values High cardinality
four_year_resale_value is highly correlated with Price_in_thousands and 6 other fieldsHigh correlation
Price_in_thousands is highly correlated with four_year_resale_value and 6 other fieldsHigh correlation
Engine_size is highly correlated with four_year_resale_value and 9 other fieldsHigh correlation
Horsepower is highly correlated with four_year_resale_value and 8 other fieldsHigh correlation
Wheelbase is highly correlated with Engine_size and 5 other fieldsHigh correlation
Width is highly correlated with Engine_size and 7 other fieldsHigh correlation
Length is highly correlated with Engine_size and 5 other fieldsHigh correlation
Curb_weight is highly correlated with four_year_resale_value and 9 other fieldsHigh correlation
Fuel_capacity is highly correlated with four_year_resale_value and 9 other fieldsHigh correlation
Fuel_efficiency is highly correlated with four_year_resale_value and 8 other fieldsHigh correlation
Power_perf_factor is highly correlated with four_year_resale_value and 7 other fieldsHigh correlation
four_year_resale_value is highly correlated with Price_in_thousands and 3 other fieldsHigh correlation
Price_in_thousands is highly correlated with four_year_resale_value and 4 other fieldsHigh correlation
Engine_size is highly correlated with four_year_resale_value and 8 other fieldsHigh correlation
Horsepower is highly correlated with four_year_resale_value and 6 other fieldsHigh correlation
Wheelbase is highly correlated with Width and 3 other fieldsHigh correlation
Width is highly correlated with Engine_size and 7 other fieldsHigh correlation
Length is highly correlated with Engine_size and 4 other fieldsHigh correlation
Curb_weight is highly correlated with Price_in_thousands and 8 other fieldsHigh correlation
Fuel_capacity is highly correlated with Engine_size and 6 other fieldsHigh correlation
Fuel_efficiency is highly correlated with Engine_size and 5 other fieldsHigh correlation
Power_perf_factor is highly correlated with four_year_resale_value and 7 other fieldsHigh correlation
four_year_resale_value is highly correlated with Price_in_thousands and 2 other fieldsHigh correlation
Price_in_thousands is highly correlated with four_year_resale_value and 3 other fieldsHigh correlation
Engine_size is highly correlated with Horsepower and 5 other fieldsHigh correlation
Horsepower is highly correlated with four_year_resale_value and 5 other fieldsHigh correlation
Wheelbase is highly correlated with Width and 2 other fieldsHigh correlation
Width is highly correlated with Engine_size and 3 other fieldsHigh correlation
Length is highly correlated with Wheelbase and 2 other fieldsHigh correlation
Curb_weight is highly correlated with Price_in_thousands and 8 other fieldsHigh correlation
Fuel_capacity is highly correlated with Engine_size and 2 other fieldsHigh correlation
Fuel_efficiency is highly correlated with Engine_size and 4 other fieldsHigh correlation
Power_perf_factor is highly correlated with four_year_resale_value and 5 other fieldsHigh correlation
Manufacturer is highly correlated with four_year_resale_value and 7 other fieldsHigh correlation
Sales_in_thousands is highly correlated with Wheelbase and 1 other fieldsHigh correlation
four_year_resale_value is highly correlated with Manufacturer and 5 other fieldsHigh correlation
Vehicle_type is highly correlated with Wheelbase and 4 other fieldsHigh correlation
Price_in_thousands is highly correlated with Manufacturer and 8 other fieldsHigh correlation
Engine_size is highly correlated with Manufacturer and 9 other fieldsHigh correlation
Horsepower is highly correlated with Manufacturer and 9 other fieldsHigh correlation
Wheelbase is highly correlated with Sales_in_thousands and 8 other fieldsHigh correlation
Width is highly correlated with Vehicle_type and 8 other fieldsHigh correlation
Length is highly correlated with Manufacturer and 7 other fieldsHigh correlation
Curb_weight is highly correlated with four_year_resale_value and 10 other fieldsHigh correlation
Fuel_capacity is highly correlated with Manufacturer and 11 other fieldsHigh correlation
Fuel_efficiency is highly correlated with Manufacturer and 9 other fieldsHigh correlation
Power_perf_factor is highly correlated with Manufacturer and 8 other fieldsHigh correlation
four_year_resale_value has 36 (22.9%) missing values Missing
Price_in_thousands has 2 (1.3%) missing values Missing
Curb_weight has 2 (1.3%) missing values Missing
Fuel_efficiency has 3 (1.9%) missing values Missing
Power_perf_factor has 2 (1.3%) missing values Missing
Model is uniformly distributed Uniform
Latest_Launch is uniformly distributed Uniform
Sales_in_thousands has unique values Unique

Reproduction

Analysis started2022-04-21 16:17:50.268776
Analysis finished2022-04-21 16:18:37.768508
Duration47.5 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Manufacturer
Categorical

HIGH CORRELATION

Distinct30
Distinct (%)19.1%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
Dodge
11 
Ford
11 
Toyota
 
9
Chevrolet
 
9
Mercedes-B
 
9
Other values (25)
108 

Length

Max length10
Median length6
Mean length6.707006369
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.3%

Sample

1st rowAcura
2nd rowAcura
3rd rowAcura
4th rowAcura
5th rowAudi

Common Values

ValueCountFrequency (%)
Dodge11
 
7.0%
Ford11
 
7.0%
Toyota9
 
5.7%
Chevrolet9
 
5.7%
Mercedes-B9
 
5.7%
Mitsubishi7
 
4.5%
Nissan7
 
4.5%
Chrysler7
 
4.5%
Volvo6
 
3.8%
Oldsmobile6
 
3.8%
Other values (20)75
47.8%

Length

2022-04-21T21:48:38.000614image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dodge11
 
7.0%
ford11
 
7.0%
toyota9
 
5.7%
chevrolet9
 
5.7%
mercedes-b9
 
5.7%
mitsubishi7
 
4.5%
nissan7
 
4.5%
chrysler7
 
4.5%
volvo6
 
3.8%
oldsmobile6
 
3.8%
Other values (20)75
47.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Model
Categorical

HIGH CARDINALITY
UNIFORM

Distinct156
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
Neon
 
2
Integra
 
1
Cutlass
 
1
Sentra
 
1
Altima
 
1
Other values (151)
151 

Length

Max length14
Median length6
Mean length6.554140127
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique155 ?
Unique (%)98.7%

Sample

1st rowIntegra
2nd rowTL
3rd rowCL
4th rowRL
5th rowA4

Common Values

ValueCountFrequency (%)
Neon2
 
1.3%
Integra1
 
0.6%
Cutlass1
 
0.6%
Sentra1
 
0.6%
Altima1
 
0.6%
Maxima1
 
0.6%
Quest1
 
0.6%
Pathfinder1
 
0.6%
Xterra1
 
0.6%
Frontier1
 
0.6%
Other values (146)146
93.0%

Length

2022-04-21T21:48:38.265903image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
grand4
 
2.3%
ram3
 
1.7%
coupe3
 
1.7%
town2
 
1.1%
cherokee2
 
1.1%
cabrio2
 
1.1%
montero2
 
1.1%
carrera2
 
1.1%
neon2
 
1.1%
sebring2
 
1.1%
Other values (153)153
86.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Sales_in_thousands
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct157
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.99807643
Minimum0.11
Maximum540.561
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2022-04-21T21:48:38.568170image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.11
5-th percentile1.8708
Q114.114
median29.45
Q367.956
95-th percentile185.3362
Maximum540.561
Range540.451
Interquartile range (IQR)53.842

Descriptive statistics

Standard deviation68.029422
Coefficient of variation (CV)1.283620587
Kurtosis17.55734423
Mean52.99807643
Median Absolute Deviation (MAD)20.468
Skewness3.408518366
Sum8320.698
Variance4628.002257
MonotonicityNot monotonic
2022-04-21T21:48:38.894663image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.9191
 
0.6%
1.1121
 
0.6%
42.6431
 
0.6%
88.0941
 
0.6%
79.8531
 
0.6%
27.3081
 
0.6%
42.5741
 
0.6%
54.1581
 
0.6%
65.0051
 
0.6%
38.5541
 
0.6%
Other values (147)147
93.6%
ValueCountFrequency (%)
0.111
0.6%
0.9161
0.6%
0.9541
0.6%
1.1121
0.6%
1.281
0.6%
1.381
0.6%
1.5261
0.6%
1.8661
0.6%
1.8721
0.6%
3.3111
0.6%
ValueCountFrequency (%)
540.5611
0.6%
276.7471
0.6%
247.9941
0.6%
245.8151
0.6%
230.9021
0.6%
227.0611
0.6%
220.651
0.6%
199.6851
0.6%
181.7491
0.6%
175.671
0.6%

four_year_resale_value
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct117
Distinct (%)96.7%
Missing36
Missing (%)22.9%
Infinite0
Infinite (%)0.0%
Mean18.07297521
Minimum5.16
Maximum67.55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2022-04-21T21:48:39.259398image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum5.16
5-th percentile7.85
Q111.26
median14.18
Q319.875
95-th percentile41.25
Maximum67.55
Range62.39
Interquartile range (IQR)8.615

Descriptive statistics

Standard deviation11.4533841
Coefficient of variation (CV)0.6337298629
Kurtosis5.763855916
Mean18.07297521
Median Absolute Deviation (MAD)3.96
Skewness2.294915493
Sum2186.83
Variance131.1800073
MonotonicityNot monotonic
2022-04-21T21:48:39.561575image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.752
 
1.3%
18.2252
 
1.3%
16.5752
 
1.3%
12.0252
 
1.3%
41.451
 
0.6%
20.431
 
0.6%
14.7951
 
0.6%
26.051
 
0.6%
58.61
 
0.6%
50.3751
 
0.6%
Other values (107)107
68.2%
(Missing)36
 
22.9%
ValueCountFrequency (%)
5.161
0.6%
5.861
0.6%
7.4251
0.6%
7.752
1.3%
7.8251
0.6%
7.851
0.6%
8.3251
0.6%
8.451
0.6%
8.81
0.6%
8.8351
0.6%
ValueCountFrequency (%)
67.551
0.6%
60.6251
0.6%
58.61
0.6%
58.471
0.6%
50.3751
0.6%
41.451
0.6%
41.251
0.6%
40.3751
0.6%
391
0.6%
36.2251
0.6%

Vehicle_type
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
Passenger
116 
Car
41 

Length

Max length9
Median length9
Mean length7.433121019
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPassenger
2nd rowPassenger
3rd rowPassenger
4th rowPassenger
5th rowPassenger

Common Values

ValueCountFrequency (%)
Passenger116
73.9%
Car41
 
26.1%

Length

2022-04-21T21:48:39.864815image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-21T21:48:40.023436image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
passenger116
73.9%
car41
 
26.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Price_in_thousands
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct152
Distinct (%)98.1%
Missing2
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean27.39075484
Minimum9.235
Maximum85.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2022-04-21T21:48:40.240877image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum9.235
5-th percentile12.469
Q118.0175
median22.799
Q331.9475
95-th percentile55.835
Maximum85.5
Range76.265
Interquartile range (IQR)13.93

Descriptive statistics

Standard deviation14.35165319
Coefficient of variation (CV)0.5239597548
Kurtosis3.63041233
Mean27.39075484
Median Absolute Deviation (MAD)6.099
Skewness1.765734331
Sum4245.567
Variance205.9699493
MonotonicityNot monotonic
2022-04-21T21:48:40.588007image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.892
 
1.3%
38.92
 
1.3%
12.642
 
1.3%
24.151
 
0.6%
20.391
 
0.6%
26.2491
 
0.6%
26.3991
 
0.6%
29.2991
 
0.6%
22.7991
 
0.6%
17.891
 
0.6%
Other values (142)142
90.4%
(Missing)2
 
1.3%
ValueCountFrequency (%)
9.2351
0.6%
9.6991
0.6%
10.6851
0.6%
11.5281
0.6%
11.7991
0.6%
12.051
0.6%
12.071
0.6%
12.3151
0.6%
12.5351
0.6%
12.642
1.3%
ValueCountFrequency (%)
85.51
0.6%
82.61
0.6%
74.971
0.6%
71.021
0.6%
69.7251
0.6%
69.71
0.6%
621
0.6%
60.1051
0.6%
54.0051
0.6%
51.7281
0.6%

Engine_size
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct31
Distinct (%)19.9%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean3.060897436
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2022-04-21T21:48:40.901480image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.8
Q12.3
median3
Q33.575
95-th percentile4.775
Maximum8
Range7
Interquartile range (IQR)1.275

Descriptive statistics

Standard deviation1.044652973
Coefficient of variation (CV)0.3412897672
Kurtosis2.344782023
Mean3.060897436
Median Absolute Deviation (MAD)0.7
Skewness1.100447343
Sum477.5
Variance1.091299835
MonotonicityNot monotonic
2022-04-21T21:48:41.320016image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
217
 
10.8%
314
 
8.9%
2.411
 
7.0%
2.511
 
7.0%
4.69
 
5.7%
1.88
 
5.1%
3.58
 
5.1%
3.88
 
5.1%
47
 
4.5%
3.47
 
4.5%
Other values (21)56
35.7%
ValueCountFrequency (%)
11
 
0.6%
1.51
 
0.6%
1.61
 
0.6%
1.88
5.1%
1.95
 
3.2%
217
10.8%
2.24
 
2.5%
2.36
 
3.8%
2.411
7.0%
2.511
7.0%
ValueCountFrequency (%)
81
 
0.6%
5.72
 
1.3%
5.41
 
0.6%
5.22
 
1.3%
52
 
1.3%
4.72
 
1.3%
4.69
5.7%
4.32
 
1.3%
4.21
 
0.6%
47
4.5%

Horsepower
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct66
Distinct (%)42.3%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean185.9487179
Minimum55
Maximum450
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2022-04-21T21:48:41.672357image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum55
5-th percentile114.5
Q1149.5
median177.5
Q3215
95-th percentile300
Maximum450
Range395
Interquartile range (IQR)65.5

Descriptive statistics

Standard deviation56.70032086
Coefficient of variation (CV)0.3049245054
Kurtosis2.406657478
Mean185.9487179
Median Absolute Deviation (MAD)32.5
Skewness1.000694992
Sum29008
Variance3214.926385
MonotonicityNot monotonic
2022-04-21T21:48:42.017431image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1509
 
5.7%
1709
 
5.7%
2008
 
5.1%
2107
 
4.5%
1156
 
3.8%
1855
 
3.2%
1755
 
3.2%
2755
 
3.2%
1204
 
2.5%
1904
 
2.5%
Other values (56)94
59.9%
ValueCountFrequency (%)
551
 
0.6%
921
 
0.6%
1002
 
1.3%
1061
 
0.6%
1071
 
0.6%
1101
 
0.6%
1131
 
0.6%
1156
3.8%
1191
 
0.6%
1204
2.5%
ValueCountFrequency (%)
4501
 
0.6%
3451
 
0.6%
3101
 
0.6%
3022
 
1.3%
3004
2.5%
2901
 
0.6%
2755
3.2%
2551
 
0.6%
2533
1.9%
2501
 
0.6%

Wheelbase
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct88
Distinct (%)56.4%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean107.4871795
Minimum92.6
Maximum138.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2022-04-21T21:48:42.384717image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum92.6
5-th percentile95.875
Q1103
median107
Q3112.2
95-th percentile119.25
Maximum138.7
Range46.1
Interquartile range (IQR)9.2

Descriptive statistics

Standard deviation7.64130303
Coefficient of variation (CV)0.07109036693
Kurtosis2.859284871
Mean107.4871795
Median Absolute Deviation (MAD)4.6
Skewness0.9699356566
Sum16768
Variance58.38951199
MonotonicityNot monotonic
2022-04-21T21:48:42.752347image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
112.28
 
5.1%
1075
 
3.2%
1135
 
3.2%
102.44
 
2.5%
1094
 
2.5%
106.54
 
2.5%
1084
 
2.5%
98.94
 
2.5%
106.44
 
2.5%
107.34
 
2.5%
Other values (78)110
70.1%
ValueCountFrequency (%)
92.62
1.3%
93.11
0.6%
93.41
0.6%
94.52
1.3%
94.91
0.6%
95.21
0.6%
96.11
0.6%
96.21
0.6%
971
0.6%
97.11
0.6%
ValueCountFrequency (%)
138.71
0.6%
138.51
0.6%
1311
0.6%
127.21
0.6%
121.51
0.6%
120.71
0.6%
1202
1.3%
1192
1.3%
118.11
0.6%
117.71
0.6%

Width
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct78
Distinct (%)50.0%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean71.15
Minimum62.6
Maximum79.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2022-04-21T21:48:43.087863image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum62.6
5-th percentile66.5
Q168.4
median70.55
Q373.425
95-th percentile78.2
Maximum79.9
Range17.3
Interquartile range (IQR)5.025

Descriptive statistics

Standard deviation3.451871862
Coefficient of variation (CV)0.0485154162
Kurtosis-0.3004675291
Mean71.15
Median Absolute Deviation (MAD)2.4
Skewness0.4838620694
Sum11099.4
Variance11.91541935
MonotonicityNot monotonic
2022-04-21T21:48:43.381952image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66.76
 
3.8%
74.46
 
3.8%
68.35
 
3.2%
70.35
 
3.2%
72.75
 
3.2%
69.14
 
2.5%
69.44
 
2.5%
66.54
 
2.5%
67.53
 
1.9%
73.63
 
1.9%
Other values (68)111
70.7%
ValueCountFrequency (%)
62.61
 
0.6%
65.71
 
0.6%
66.43
1.9%
66.54
2.5%
66.76
3.8%
66.92
 
1.3%
671
 
0.6%
67.11
 
0.6%
67.32
 
1.3%
67.41
 
0.6%
ValueCountFrequency (%)
79.91
 
0.6%
79.31
 
0.6%
79.11
 
0.6%
78.82
1.3%
78.71
 
0.6%
78.23
1.9%
771
 
0.6%
76.82
1.3%
76.61
 
0.6%
76.42
1.3%

Length
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct127
Distinct (%)81.4%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean187.3435897
Minimum149.4
Maximum224.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2022-04-21T21:48:43.642795image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum149.4
5-th percentile163.675
Q1177.575
median187.9
Q3196.125
95-th percentile208.5
Maximum224.5
Range75.1
Interquartile range (IQR)18.55

Descriptive statistics

Standard deviation13.43175428
Coefficient of variation (CV)0.07169583065
Kurtosis0.3025740191
Mean187.3435897
Median Absolute Deviation (MAD)9.4
Skewness-0.05904682307
Sum29225.6
Variance180.4120232
MonotonicityNot monotonic
2022-04-21T21:48:43.965387image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
186.34
 
2.5%
189.23
 
1.9%
1923
 
1.9%
190.43
 
1.9%
163.32
 
1.3%
192.52
 
1.3%
1742
 
1.3%
2122
 
1.3%
193.52
 
1.3%
208.52
 
1.3%
Other values (117)131
83.4%
ValueCountFrequency (%)
149.41
0.6%
1521
0.6%
157.31
0.6%
157.91
0.6%
160.41
0.6%
161.11
0.6%
163.32
1.3%
163.81
0.6%
165.41
0.6%
166.71
0.6%
ValueCountFrequency (%)
224.51
0.6%
224.21
0.6%
215.31
0.6%
2151
0.6%
2122
1.3%
209.11
0.6%
208.52
1.3%
207.71
0.6%
207.21
0.6%
206.81
0.6%

Curb_weight
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct147
Distinct (%)94.8%
Missing2
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean3.378025806
Minimum1.895
Maximum5.572
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2022-04-21T21:48:44.232090image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.895
5-th percentile2.4235
Q12.971
median3.342
Q33.7995
95-th percentile4.3891
Maximum5.572
Range3.677
Interquartile range (IQR)0.8285

Descriptive statistics

Standard deviation0.6305016344
Coefficient of variation (CV)0.1866479626
Kurtosis1.265453569
Mean3.378025806
Median Absolute Deviation (MAD)0.41
Skewness0.7081582404
Sum523.594
Variance0.397532311
MonotonicityNot monotonic
2022-04-21T21:48:44.500625image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.7693
 
1.9%
2.9983
 
1.9%
2.912
 
1.3%
3.0752
 
1.3%
3.8762
 
1.3%
3.3682
 
1.3%
3.4551
 
0.6%
2.9581
 
0.6%
3.1021
 
0.6%
4.3871
 
0.6%
Other values (137)137
87.3%
(Missing)2
 
1.3%
ValueCountFrequency (%)
1.8951
0.6%
2.241
0.6%
2.251
0.6%
2.3321
0.6%
2.3391
0.6%
2.3671
0.6%
2.3981
0.6%
2.421
0.6%
2.4251
0.6%
2.4521
0.6%
ValueCountFrequency (%)
5.5721
0.6%
5.4011
0.6%
5.3931
0.6%
5.1151
0.6%
4.8081
0.6%
4.521
0.6%
4.471
0.6%
4.3941
0.6%
4.3871
0.6%
4.2981
0.6%

Fuel_capacity
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct55
Distinct (%)35.3%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean17.95192308
Minimum10.3
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2022-04-21T21:48:44.840775image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum10.3
5-th percentile12.5
Q115.8
median17.2
Q319.575
95-th percentile25.4
Maximum32
Range21.7
Interquartile range (IQR)3.775

Descriptive statistics

Standard deviation3.887921265
Coefficient of variation (CV)0.2165740822
Kurtosis2.07281321
Mean17.95192308
Median Absolute Deviation (MAD)1.9
Skewness1.136712406
Sum2800.5
Variance15.11593176
MonotonicityNot monotonic
2022-04-21T21:48:45.161988image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.514
 
8.9%
179
 
5.7%
208
 
5.1%
198
 
5.1%
167
 
4.5%
13.26
 
3.8%
15.96
 
3.8%
155
 
3.2%
14.55
 
3.2%
17.55
 
3.2%
Other values (45)83
52.9%
ValueCountFrequency (%)
10.31
 
0.6%
11.92
 
1.3%
121
 
0.6%
12.13
1.9%
12.52
 
1.3%
12.71
 
0.6%
13.12
 
1.3%
13.26
3.8%
13.71
 
0.6%
141
 
0.6%
ValueCountFrequency (%)
322
1.3%
302
1.3%
263
1.9%
25.42
1.3%
25.11
 
0.6%
253
1.9%
24.31
 
0.6%
23.71
 
0.6%
23.22
1.3%
22.51
 
0.6%

Fuel_efficiency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct20
Distinct (%)13.0%
Missing3
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean23.84415584
Minimum15
Maximum45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2022-04-21T21:48:45.453359image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile16.65
Q121
median24
Q326
95-th percentile31
Maximum45
Range30
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.282705562
Coefficient of variation (CV)0.1796123792
Kurtosis3.241130815
Mean23.84415584
Median Absolute Deviation (MAD)2
Skewness0.6923277567
Sum3672
Variance18.34156693
MonotonicityNot monotonic
2022-04-21T21:48:45.703575image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2523
14.6%
2416
10.2%
2715
9.6%
2214
8.9%
2114
8.9%
2314
8.9%
2612
 
7.6%
196
 
3.8%
205
 
3.2%
155
 
3.2%
Other values (10)30
19.1%
ValueCountFrequency (%)
155
 
3.2%
163
 
1.9%
173
 
1.9%
185
 
3.2%
196
 
3.8%
205
 
3.2%
2114
8.9%
2214
8.9%
2314
8.9%
2416
10.2%
ValueCountFrequency (%)
451
 
0.6%
334
 
2.5%
321
 
0.6%
313
 
1.9%
305
 
3.2%
292
 
1.3%
283
 
1.9%
2715
9.6%
2612
7.6%
2523
14.6%

Latest_Launch
Categorical

HIGH CARDINALITY
UNIFORM

Distinct130
Distinct (%)82.8%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
10/5/2012
 
2
1/24/2011
 
2
4/26/2011
 
2
6/25/2011
 
2
9/21/2011
 
2
Other values (125)
147 

Length

Max length10
Median length9
Mean length8.968152866
Min length8

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique103 ?
Unique (%)65.6%

Sample

1st row2/2/2012
2nd row6/3/2011
3rd row1/4/2012
4th row3/10/2011
5th row10/8/2011

Common Values

ValueCountFrequency (%)
10/5/20122
 
1.3%
1/24/20112
 
1.3%
4/26/20112
 
1.3%
6/25/20112
 
1.3%
9/21/20112
 
1.3%
2/18/20112
 
1.3%
4/1/20112
 
1.3%
5/31/20112
 
1.3%
8/27/20112
 
1.3%
9/25/20112
 
1.3%
Other values (120)137
87.3%

Length

2022-04-21T21:48:46.029009image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
10/5/20122
 
1.3%
8/31/20112
 
1.3%
1/24/20112
 
1.3%
10/30/20122
 
1.3%
2/23/20122
 
1.3%
11/24/20122
 
1.3%
8/16/20122
 
1.3%
1/4/20122
 
1.3%
9/10/20122
 
1.3%
4/24/20112
 
1.3%
Other values (120)137
87.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Power_perf_factor
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct154
Distinct (%)99.4%
Missing2
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean77.0435912
Minimum23.27627233
Maximum188.144323
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2022-04-21T21:48:46.321268image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum23.27627233
5-th percentile46.2039974
Q160.40770678
median72.03091719
Q389.41487752
95-th percentile125.0915129
Maximum188.144323
Range164.8680507
Interquartile range (IQR)29.00717075

Descriptive statistics

Standard deviation25.1426641
Coefficient of variation (CV)0.3263433558
Kurtosis2.081292892
Mean77.0435912
Median Absolute Deviation (MAD)14.24160572
Skewness1.070634989
Sum11941.75664
Variance632.153558
MonotonicityNot monotonic
2022-04-21T21:48:46.840093image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52.084898752
 
1.3%
58.280149521
 
0.6%
86.272522911
 
0.6%
63.313727831
 
0.6%
89.427820311
 
0.6%
71.171664131
 
0.6%
72.290355081
 
0.6%
69.782944341
 
0.6%
67.889270591
 
0.6%
60.861611551
 
0.6%
Other values (144)144
91.7%
(Missing)2
 
1.3%
ValueCountFrequency (%)
23.276272331
0.6%
36.672283581
0.6%
39.986424751
0.6%
40.700072421
0.6%
42.879097341
0.6%
43.117132011
0.6%
44.083709461
0.6%
45.832180561
0.6%
46.363347471
0.6%
46.943876761
0.6%
ValueCountFrequency (%)
188.1443231
0.6%
141.141151
0.6%
141.10098451
0.6%
139.98229361
0.6%
135.91470961
0.6%
134.65685821
0.6%
134.39097541
0.6%
125.27387571
0.6%
125.01335741
0.6%
124.44671631
0.6%

Interactions

2022-04-21T21:48:31.700738image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:47:54.589131image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:47:57.606734image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:01.077899image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:04.522524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:07.987253image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:11.375848image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:14.547152image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:17.902083image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:21.419895image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:25.161625image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:28.467552image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:31.995548image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:47:54.881520image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:47:57.992491image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:01.401995image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:04.806084image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:08.252612image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:11.566855image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:14.796532image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:18.146596image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:21.675288image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:25.467879image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:28.763375image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:32.361675image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:47:55.161821image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:47:58.257491image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:01.678257image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:05.061077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:08.526942image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:11.795784image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:15.101352image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:18.421858image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:22.044280image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:25.744327image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:29.048284image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:32.664429image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:47:55.535233image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:47:58.528377image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:01.999547image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:05.503890image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:08.814510image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:11.991487image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:15.426967image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:18.691307image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:22.318588image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:26.028568image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:29.332525image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:32.966108image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:47:55.782074image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:47:58.779739image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:02.284519image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:05.787155image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:09.097845image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:12.340797image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:15.759688image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:18.936653image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:22.646395image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:26.347795image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:29.611976image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:33.240600image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:47:55.993729image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:47:59.118049image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:02.590343image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:06.088350image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:09.397211image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:12.677871image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:16.034914image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:19.247423image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:22.927767image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:26.694969image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:29.874274image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:33.466001image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:47:56.189645image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:47:59.390077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:02.826769image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:06.343699image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:09.657627image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:12.946507image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:16.265604image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:19.725256image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:23.166707image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:26.952311image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:30.129593image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:33.797644image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:47:56.397736image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:47:59.707885image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:03.108145image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:06.635933image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:09.961441image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:13.251932image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:16.530746image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:20.025425image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:23.489918image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:27.252475image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:30.375197image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:34.087890image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:47:56.603189image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:47:59.962191image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:03.371125image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:06.887390image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:10.307842image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:13.508284image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:16.780085image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:20.314675image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:23.847105image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:27.651282image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:30.656487image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:34.386565image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:47:56.875492image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:00.250430image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:03.662487image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:07.194705image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:10.586625image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:13.776577image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:17.070304image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:20.607533image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:24.170912image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:27.849236image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:30.940926image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:34.846557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:47:57.100942image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:00.519884image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:03.895852image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:07.435251image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:10.836030image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:14.012006image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:17.349872image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:20.858270image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:24.453373image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:28.070395image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:31.168317image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:35.096522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:47:57.344396image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:00.783286image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:04.195749image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:07.723348image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:11.141867image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:14.297243image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:17.621109image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:21.135600image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:24.797953image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:28.278466image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-21T21:48:31.423180image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-04-21T21:48:47.160236image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-21T21:48:47.683949image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-21T21:48:48.189103image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-21T21:48:48.685924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-04-21T21:48:48.972590image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-21T21:48:35.652179image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-04-21T21:48:36.347351image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-04-21T21:48:36.950779image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-04-21T21:48:37.494080image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

ManufacturerModelSales_in_thousandsfour_year_resale_valueVehicle_typePrice_in_thousandsEngine_sizeHorsepowerWheelbaseWidthLengthCurb_weightFuel_capacityFuel_efficiencyLatest_LaunchPower_perf_factor
0AcuraIntegra16.91916.360Passenger21.501.8140.0101.267.3172.42.63913.228.02/2/201258.280150
1AcuraTL39.38419.875Passenger28.403.2225.0108.170.3192.93.51717.225.06/3/201191.370778
2AcuraCL14.11418.225PassengerNaN3.2225.0106.970.6192.03.47017.226.01/4/2012NaN
3AcuraRL8.58829.725Passenger42.003.5210.0114.671.4196.63.85018.022.03/10/201191.389779
4AudiA420.39722.255Passenger23.991.8150.0102.668.2178.02.99816.427.010/8/201162.777639
5AudiA618.78023.555Passenger33.952.8200.0108.776.1192.03.56118.522.08/9/201184.565105
6AudiA81.38039.000Passenger62.004.2310.0113.074.0198.23.90223.721.02/27/2012134.656858
7BMW323i19.747NaNPassenger26.992.5170.0107.368.4176.03.17916.626.06/28/201171.191207
8BMW328i9.23128.675Passenger33.402.8193.0107.368.5176.03.19716.624.01/29/201281.877069
9BMW528i17.52736.125Passenger38.902.8193.0111.470.9188.03.47218.525.04/4/201183.998724

Last rows

ManufacturerModelSales_in_thousandsfour_year_resale_valueVehicle_typePrice_in_thousandsEngine_sizeHorsepowerWheelbaseWidthLengthCurb_weightFuel_capacityFuel_efficiencyLatest_LaunchPower_perf_factor
147VolkswagenPassat51.10216.725Passenger21.201.8150.0106.468.5184.13.04316.427.010/30/201261.701381
148VolkswagenCabrio9.56916.575Passenger19.992.0115.097.466.7160.43.07913.726.05/31/201148.907372
149VolkswagenGTI5.59613.760Passenger17.502.0115.098.968.3163.32.76214.626.04/1/201147.946841
150VolkswagenBeetle49.463NaNPassenger15.902.0115.098.967.9161.12.76914.526.010/20/201147.329632
151VolvoS4016.957NaNPassenger23.401.9160.0100.567.6176.62.99815.825.02/18/201166.113057
152VolvoV403.545NaNPassenger24.401.9160.0100.567.6176.63.04215.825.09/21/201166.498812
153VolvoS7015.245NaNPassenger27.502.4168.0104.969.3185.93.20817.925.011/24/201270.654495
154VolvoV7017.531NaNPassenger28.802.4168.0104.969.3186.23.25917.925.06/25/201171.155978
155VolvoC703.493NaNPassenger45.502.3236.0104.971.5185.73.60118.523.04/26/2011101.623357
156VolvoS8018.969NaNPassenger36.002.9201.0109.972.1189.83.60021.124.011/14/201185.735655